Many researchers have worked on ecological forecasting models in agricultural domain for the applications such as forecasting of pest or disease infestation, yield prediction, prediction of harvesting time, prediction of growth of plants etc. Recently some researchers have proposed data assimilation techniques in agriculture to improve the estimates of the model parameters for optimization of deviation between observed and model outcome. But mostly remote sensing data are integrated to improve the model simulation. But lots of heterogeneity is involved from field to field which cannot be overcome by just considering the weather or remote sensing data. There is need to integrate information at various scales like field, village, watershed, regional which covers field level heterogeneity in terms of region as well as the framework can be easily be adopted to the various crops.
Mostly the available forecasting models are specific for a region with a particular geographical boundary of applicability. The existing models do not consider the field specific activities (like irrigation type, chemical usage, different other farm operations etc) and field specific weather parameters (like soil characteristics, humidity at field level), which may result in an impact on the forecast model. So, field specific models are needed for the ecological forecasting.
Prior art does not focuses on the effect of farm operations followed during the growth season of the crop while developing the forecasting model.